Transformer architectures
have proven extremely adept at modeling long-term relationships
within an input sequence via attention mechanisms.
Originally used for language tasks [1]}, [2]}, [3]},
they have since been adapted for solving popular computer vision problems
such as image classification [4]},
generation [5]}, [6]},
video understanding [7]}, [8]}, [9]},
object detection [10]},
and zero-shot classification [11]}.
In this work, we take inspiration from a specific variant of self-attention,
namely slot attention [12]},
which was demonstrated to be effective for learning object-centric representations on synthetic datasets,  CLEVR.
